---
title: "Dashboard with parameters"
output:
flexdashboard::flex_dashboard:
source_code: embed
params:
loanbook: "~/Downloads/data/from-bank/loanbook.csv"
abcd: "~/Downloads/data/from-2dii/abcd.csv"
scenario: "~/Downloads/data/from-2dii/scenario.csv"
regions: "~/Downloads/data/from-2dii/regions.csv"
to_validate: "~/Downloads/data/validation/to_validate.csv"
validated: "~/Downloads/data/validation/validated.csv"
sector: "oil and gas"
---
```{r setup, include=FALSE}
library(flexdashboard)
options(readr.show_col_types = FALSE)
show_code <- FALSE
```
# Page 1
Column {data-width=300}
-----------------------------------------------------------------------
### r2dii workflow
Because tilt's software is still in development, we can't show its workflow from
end to end. But tilt's software is very similar to [r2dii's
software](https://pacta.rmi.org/pacta-for-banks-2020/), so we'll show it
instead.
Both tilt and r2dii workflows share common steps:
1. Input bank data.
2 Input 2DII data.
3. Match bank and 2DII data.
4. Validate matched companies.
5. Calculate a metric using 2DII's methodology.
6. Visualize the metric.
```{r echo=show_code}
# r2dii software
# * Toy data from banks and from tilt data-management. Eventually tiltToyData.
library(r2dii.data)
# * Like tilt.company.match, eventually tiltMatch
library(r2dii.match)
# * Like tiltIndicator
library(r2dii.analysis)
# * Like tiltPlot
library(r2dii.plot)
# General software
library(tidyverse, warn.conflicts = FALSE)
library(DT)
```
Column {data-width=700 .tabset}
-----------------------------------------------------------------------
### 1. Bank data
```{r echo=show_code}
loanbook <- read_csv(params$loanbook)
```
```{r}
datatable(loanbook)
```
> Private data from the bank.
### 2. 2DII data
```{r echo=show_code}
abcd <- read_csv(params$abcd)
```
```{r}
datatable(abcd)
```
> Data from 2DII.
### 3. Matched data
```{r echo=show_code}
matched <- match_name(loanbook, abcd)
```
```{r}
datatable(matched)
```
> Bank data matched to 2DII data.
### 3. Validated data
```{r echo=show_code}
# The bank saves the matched data to validate it manually.
write_csv(matched, params$to_validate)
```
```{r echo=show_code}
validated <- read_csv(params$validated)
```
```{r echo=show_code}
# Where the number of perfect matches is greater than one, pick the one with
# highest priority
validated_and_prioritized <- prioritize(validated)
```
```{r}
datatable(validated_and_prioritized)
```
> Data manually validated and prioritized by the bank.
### 4. Calculated metric
```{r echo=show_code}
# Datasets that the bank gets from a data provider
scenario <- read_csv(params$scenario)
regions <- read_csv(params$regions)
metric <- validated_and_prioritized |>
target_market_share(abcd = abcd, scenario = scenario, region_isos = regions)
```
```{r}
datatable(metric)
```
> Metric calculated using 2DII's methodology.
### 5. Visualization
```{r echo=show_code}
# Pick the targets you want to plot
picked <- filter(
metric,
scenario_source == "demo_2020",
sector == params$sector,
region == "global",
metric %in% c("projected", "corporate_economy", "target_sds")
)
```
```{r}
# Plot the technology mix
qplot_techmix(picked)
```
> Plot showing the technology mix.
